Add devices.has_triton() and enable NNCF compile if triton is available

modules/devices.py

@@ -58,6 +58,14 @@ def has_zluda() -> bool:
         return False
 
 
+def has_triton() -> bool:
+    try:
+        from torch.utils._triton import has_triton as torch_has_triton
+        return torch_has_triton()
+    except Exception:
+        return False
+
+
 def get_backend(shared_cmd_opts):
     global args # pylint: disable=global-statement
     args = shared_cmd_opts

modules/shared.py

@@ -550,7 +550,7 @@ options_templates.update(options_section(('quantization', "Quantization Settings
     "nncf_quantize_mode": OptionInfo("INT8", "OpenVINO activations mode", gr.Dropdown, {"choices": ['INT8', 'FP8_E4M3', 'FP8_E5M2'], "visible": cmd_opts.use_openvino}),
     "nncf_quantize_conv_layers": OptionInfo(False, "Quantize the convolutional layers", gr.Checkbox, {"visible": native}),
     "nncf_decompress_fp32": OptionInfo(False, "Decompress using full precision", gr.Checkbox, {"visible": native}),
-    "nncf_decompress_compile": OptionInfo(False, "Decompress using torch.compile", gr.Checkbox, {"visible": native}),
+    "nncf_decompress_compile": OptionInfo(devices.has_triton(), "Decompress using torch.compile", gr.Checkbox, {"visible": native}),
     "nncf_quantize_shuffle_weights": OptionInfo(False, "Shuffle weights in post mode", gr.Checkbox, {"visible": native}),
 
     "layerwise_quantization_sep": OptionInfo("<h2>Layerwise Casting</h2>", "", gr.HTML),